Prototype extraction and adaptive OCR
Identifieur interne : 002033 ( Main/Exploration ); précédent : 002032; suivant : 002034Prototype extraction and adaptive OCR
Auteurs : Y. Xu [États-Unis] ; George Nagy (informaticien) [États-Unis]Source :
- IEEE Transactions on Pattern Analysis and Machine Intelligence [ 0162-8828 ] ; 1999.
Descripteurs français
- Pascal (Inist)
English descriptors
- KwdEn :
Abstract
To maintain OCR accuracy with decreasing quality of page image composition, production, and digitization, it is essential to tune the system to each document. We propose a prototype extraction method for document-specific OCR systems. The method automatically generates training samples from unsegmented text images and the corresponding transcripts. It is tolerant of transcription errors, so a transcript produced automatically by an imperfect omnifont OCR system can be used. The method is based on new algorithms for estimating character widths, character locations in a word, and match/nonmatch probabilities from unsegmented text. An experimental word recognition system is designed and developed to combine prototype extraction algorithms and segmentation-free word recognition. The system can adapt itself to different page images and achieve high recognition accuracy on heavily degraded print.
Affiliations:
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Le document en format XML
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Xu</name><affiliation wicri:level="1"><inist:fA14 i1="01"><s1>Hewlett-Packard Lab</s1><s2>Palo Alto CA</s2><s3>USA</s3><sZ>1 aut.</sZ></inist:fA14><country>États-Unis</country><wicri:noRegion>Hewlett-Packard Lab</wicri:noRegion></affiliation></author><author><name sortKey="Nagy, G" sort="Nagy, G" uniqKey="Nagy G" first="G." last="Nagy">George Nagy (informaticien)</name><affiliation><country>États-Unis</country><placeName><settlement type="city">Troy (New York</settlement><region type="state">État de New York</region></placeName><orgName type="lab" n="5">Institut polytechnique Rensselaer</orgName></affiliation></author></analytic><series><title level="j" type="main">IEEE Transactions on Pattern Analysis and Machine Intelligence</title><title level="j" type="abbreviated">IEEE Trans Pattern Anal Mach Intell</title><idno type="ISSN">0162-8828</idno><imprint><date when="1999">1999</date></imprint></series></biblStruct></sourceDesc><seriesStmt><title level="j" type="main">IEEE Transactions on Pattern Analysis and Machine Intelligence</title><title level="j" type="abbreviated">IEEE Trans Pattern Anal Mach Intell</title><idno type="ISSN">0162-8828</idno></seriesStmt></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Adaptive classification</term><term>Algorithms</term><term>Dynamic programming</term><term>Experiments</term><term>Feature extraction</term><term>Image analysis</term><term>Image quality</term><term>Image segmentation</term><term>Optical character recognition</term><term>Pattern matching</term><term>Template matching</term><term>Text reader</term><term>Theory</term></keywords><keywords scheme="Pascal" xml:lang="fr"><term>Théorie</term><term>Extraction caractéristique</term><term>Qualité image</term><term>Segmentation image</term><term>Algorithme</term><term>Concordance forme</term><term>Programmation dynamique</term><term>Analyse image</term><term>Reconnaissance optique caractère</term><term>Expérience</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">To maintain OCR accuracy with decreasing quality of page image composition, production, and digitization, it is essential to tune the system to each document. We propose a prototype extraction method for document-specific OCR systems. The method automatically generates training samples from unsegmented text images and the corresponding transcripts. It is tolerant of transcription errors, so a transcript produced automatically by an imperfect omnifont OCR system can be used. The method is based on new algorithms for estimating character widths, character locations in a word, and match/nonmatch probabilities from unsegmented text. An experimental word recognition system is designed and developed to combine prototype extraction algorithms and segmentation-free word recognition. The system can adapt itself to different page images and achieve high recognition accuracy on heavily degraded print.</div></front></TEI><affiliations><list><country><li>États-Unis</li></country><region><li>État de New York</li></region><settlement><li>Troy (New York</li></settlement><orgName><li>Institut polytechnique Rensselaer</li></orgName></list><tree><country name="États-Unis"><noRegion><name sortKey="Xu, Y" sort="Xu, Y" uniqKey="Xu Y" first="Y." last="Xu">Y. Xu</name></noRegion><name sortKey="Nagy, G" sort="Nagy, G" uniqKey="Nagy G" first="G." last="Nagy">George Nagy (informaticien)</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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